Module 1: Introduction to Machine Learning

• What is ML? History & applications

• ML vs AI vs Deep Learning

• Types of Machine Learning: Supervised, Unsupervised, Reinforcement

• Machine learning workflow and tools

Module 2: Python for Machine Learning

• Python basics and data structures

• Working with NumPy and Pandas

• Data visualization with Matplotlib and Seaborn

• Introduction to Jupyter Notebooks

Module 3: Data Preprocessing & Feature Engineering

• Handling missing values and outliers

• Encoding categorical variables

• Feature scaling and transformation

• Feature selection techniques

Module 4: Supervised Learning

• Linear Regression & Logistic Regression

• Decision Trees & Random Forest

• Support Vector Machines (SVM)

• k-Nearest Neighbors (k-NN)

• Model performance metrics (accuracy, precision, recall, F1-score)

Module 5: Unsupervised Learning

• Clustering algorithms: K-Means, Hierarchical, DBSCAN

• Dimensionality reduction: PCA, t-SNE

• Anomaly detection

Module 6: Model Evaluation & Tuning

• Train-test split and cross-validation

• Hyperparameter tuning (Grid Search, Random Search)

• Bias-variance trade-off

• ROC-AUC and confusion matrix analysis

Module 7: Ensemble Learning

• Bagging and Boosting techniques

• Random Forest vs XGBoost vs AdaBoost

• Voting classifiers and stacking

Module 8: Real-World Machine Learning Projects

• Customer churn prediction

• Sales forecasting model

• Movie recommendation system

• Final capstone project with end-to-end deployment

Module 9: Model Deployment

• Saving models with Pickle/Joblib

• Deploying ML models with Flask or Streamlit

• Introduction to cloud deployment (optional)

Module 10: Career & Certification Preparation

• Resume building and GitHub portfolio

• Mock interviews and project review

• Certification and job readiness guidance